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[en] It is assumed that the phenomenon under study is such that the time-to-failure may be modeled by an exponential distribution with failure rate lambda. For Bayesian analyses of the assumed model, the family of gamma distributions provides conjugate prior models for lambda. Thus, an experimenter needs to select a particular gamma model to conduct a Bayesian reliability analysis. The purpose of this report is to present a methodology that can be used to translate engineering information, experience, and judgment into a choice of a gamma prior distribution. The proposed methodology assumes that the practicing engineer can provide percentile data relating to either the failure rate or the reliability of the phenomenon being investigated. For example, the methodology will select the gamma prior distribution which conveys an engineer's belief that the failure rate lambda simultaneously satisfies the probability statements, P(lambda less than 1.0 x 10-3) equals 0.50 and P(lambda less than 1.0 x 10-5) equals 0.05. That is, two percentiles provided by an engineer are used to determine a gamma prior model which agrees with the specified percentiles. For those engineers who prefer to specify reliability percentiles rather than the failure rate percentiles illustrated above, it is possible to use the induced negative-log gamma prior distribution which satisfies the probability statements, P(R(t0) less than 0.99) equals 0.50 and P(R(t0) less than 0.99999) equals 0.95, for some operating time t0. The report also includes graphs for selected percentiles which assist an engineer in applying the procedure. 28 figures, 16 tables
[en] The calculating rules and recommendations of this standard act as guidelines for a quantitative probability analysis of failure sequences based on event trees in accodance with DIN-25419 (Pt.1)
[en] Failure analysis is a critical element in the integrated circuit manufacturing industry. This paper reviews the changing role of failure analysis and describes major techniques employed in the industry today. Several advanced failure analysis techniques that meet the challenges imposed by advancements in integrated circuit technology are described and their applications are discussed. Future trends in failure analysis needed to keep pace with the continuing advancements in integrated circuit technology are anticipated
[en] This paper applies Bayesian network to the system failure analysis, with an aim to improve knowledge representation of the uncertainty logic and multi-fault states in system failure analysis. A Bayesian network for shielded pump failure analysis is presented, conducting fault parameter learning, updating Bayesian network parameter based on new samples. Finally, through the Bayesian network inference, vulnerability in this system, the largest possible failure modes, and the fault probability are obtained. The powerful ability of Bayesian network to analyze system fault is illustrated by examples. (authors)
[en] A method for specific estimation of a component failure rate, based on specific quantitative and qualitative data other than component failures, was developed and is described in the proposed paper. The basis of the method is the Bayesian updating procedure. A prior distribution is selected from a generic database, whereas likelihood is built using fuzzy logic theory. With the proposed method, the component failure rate estimation is based on a much larger quantity of information compared to the presently used classical methods. Consequently, epistemic uncertainties, which are caused by lack of knowledge about a component or phenomenon are reduced. (author)
[en] Highlights: • A new model has been developed to analyze mixed cascading failures in network systems. • The new model offers distinct advantages to analyze the combined impact of network load dynamics and network dependency on failure. • Existing mixed cascading failures can be simulated, and the influence of different types of dependence clusters of network nodes on the robustness of network systems can also be studied. • The effects of network topology on network robustness considering mixed cascading failures are also investigated using numerical examples. A new model has been developed to analyze mixed cascading failures in network systems. The new model offers distinct advantages to analyze the combined impact of network load dynamics and network dependency on failure propagation, and to investigate specific effects of common types of network dependency on network robustness. Previous cascading failure models, focusing on network load dynamics, provide alternative approaches to analyze cascading failures in network systems. However, these studies seldom consider the combined impacts of multiple dependencies among network nodes, which can actually have a great impact on the dynamic behaviors of network systems. Thus, our new model extends previous research by taking both load dynamics and network dependency into account. Using this new model, existing mixed cascading failures can be simulated, and the influence of different types of dependence clusters of network nodes on the robustness of network systems can also be studied. The effects of network topology on network robustness considering mixed cascading failures are also investigated using numerical examples.
[en] Many modern systems have the property of coupling, which weakens the system against the outburst of failure. The risks to fail in a single layer may propagate to the entire system through inter-layer connections. In the field of propagation process, the existing literatures mainly focus on the global phenomena in coupled systems through some statistic methods, the dynamical evolution of failure risk propagation and the protection schemes for coupled systems are seldom mentioned. In this paper, we model the coupled systems using six types of coupled networks, over which the failure risk propagation occurs. Then, three cellular automata (CA) models are performed to describe the protection schemes in case of failure risk propagation. Based on a newly presented measurement, a series of experiments are conducted on the coupled networks as well as the single-layered networks, where the propagation processes with and without protection schemes are demonstrated. The results show that the failure risk propagation varies depending on the type and structure of the coupled networks. Moreover, with a small fraction of nodes protected based on some immunization strategies, the system’s robustness to the failure risk propagation is highly improved.
[en] The present study is mainly intended to offer a method for the use of the System Safety Analysis in the evaluation of the vulnerability of nuclear power plants' systems to external events, such as airplane crashes or conventional bombs. Only one safety system has been chosen out of the systems which comprise a nuclear power plant. An evaluation model has been prepared which is especially suitable for the evaluation of vulnerability of a system. The calculational tool, chosen to perform the quantitative Fault Tree Analysis (The computer program PREP/KITT-G), has been checked, and improved mainly for providing a better capability to evaluate the effects of the physical location of components on their failure mode, and a better treatment of simultaneous failure of several components. Several new original algorithms have been developed for the calculational model. Vulnerability of a nuclear plant to an external event has been analyzed in a preliminary way. Conclusions have been drawn with respect to the effect of the various parameters considered. (B.G.)
[en] Recent models for the failure behaviour of systems involving redundancy and diversity have shown that common mode failures can be accounted for in terms of the variability of the failure probability of components over operational environments. Whenever such variability is present, we can expect that the overall system reliability will be less than we could have expected if the components could have been assumed to fail independently. We generalise a model of hardware redundancy due to Hughes, [Hughes, R. P., A new approach to common cause failure. Reliab. Engng, 17 (1987) 211-236] and show that with forced diversity, this unwelcome result no longer applies: in fact it becomes theoretically possible to do better than would be the case under independence of failures. An example shows how the new model can be used to estimate redundant system reliability from component data